# How to Make a Frequency Table in R ﻿

A frequency table is very often used by statisticians when they are studying categorical data to understand how frequently a variable appears in their data set. If you plan on entering the industry as a data analyst or even if your work remotely involves the use of data to make decisions, then you will be coming across frequency tables all the time. It is therefore essential to have an understanding of how frequency tables work and how you can quickly construct one.

There are several easy ways to create an R frequency table, ranging from using the factor () and table () functions in Base R to specific packages. Good packages include ggmodels, dplyr, and epiDisplay.

In this tutorial, I will be going over some techniques of generating frequency tables using R. I’ll be using a built-in data set of R called “mtcars”. It contains information about the mileage, number of forward gears, number of carburetors and cylinders for various cars.

You can load the data set into your environment using the data() function. This isn’t always needed but is good practice to keep things organized in your code.

``> data(mtcars)``

### Generating a Frequency Table in R

The most common and straight forward method of generating a frequency table in R is through the use of the table() function. In this tutorial, I will be categorizing cars in my data set according to their number of cylinders. I’ll start by checking the range of the number of cylinders present in the cars.

``````# factor in R
> factor(mtcars\$cyl)``````

]This quickly tells me that the cars in my data set have 4, 6 or 8 cylinders. I can now use the table() function to see how many cars fall in each category of the number of cylinders.

``````# table in r - how to create a frequency table in R
> table(mtcars\$cyl)``````

This tells me that 11 cars have 4, 7 cars have 6 and 14 cars have 8 cylinders. However, something you may have noticed here is that this is a very non explanatory table and isn’t the best representation of categorical variables. Moreover, the table is not stored as a data frame and this makes further analysis and manipulation quite difficult. You could always find a way around it, but I usually think of table generation as a quick process and you shouldn’t have to spend too long making your table look better. One alternative is to use the count() function that comes as a part of the “plyr” package. If you haven’t installed it already, you can do that using the code below.

Once installed, you can load it and start using it.

``````# dplyr frequency table
> install.packages('plyr')
> library('plyr')
> count(mtcars, 'cyl') ``````

This table is a little more explanatory with the columns and rows labeled.

### Generating a More Refined Frequency Table in R

The number of ways through which you can perform a simple task in R is exhaustive and each method has its own pros and cons. Similarly, a frequency table can be generated using numerous functions, however, one other function that I’ll be going over in this tutorial comes in the “epiDisplay” package. It gives you a highly featured report of your data that includes frequency, cumulative frequencies and proportions. If, for instance, you wish to know what percentage of cars have 8 cylinders or what fraction of cars have 4 gears, this method allows you to get your answer using the same report.

One more thing you may have noticed in the earlier method was that if your data misses some labels for some of the observations, it doesn’t tell you there’s missing data. The “epiDisplay” package, however, tells you exactly how many data points are missing labels and even gives you the remaining stats of your data with and without including the missing data points. Here my data set does not have missing labels therefore, it doesn’t change much but it is an important feature of the function.

``````# how to make frequency table in r (nicer version)
> install.packages('epiDisplay')
> library(epiDisplay)
> tab1(mtcars\$cyl, sort.group = "decreasing", cum.percent = TRUE)``````

The tab1() function also prints a bar chart to show relative frequencies by default.

### Cross Tabulation Using ‘gmodels’

Up till now, we have talked about frequency (or the count of appearance) of one variable in a data set, but for data analysts, an important task would be to generate a frequency with 2, 3 or even more variables. Such a table is also called a Cross Table or a Contingency Table. When talking about frequency tables, I find it important to extend the discussion to tables with higher number of variables as well.

If I talk about the data set that I am using in this tutorial, suppose I want to know how many cars use a combination of 4 cylinders and 5 forward gears. One way would be to do this manually using two different frequency tables, but that method is quite inefficient especially if my data set had more variables.

Another method is to use the CrossTable() function from the ‘gmodels’ package. It not only gives me a frequency count but also the proportions and the chi square contribution of each category.

``````# CrossTable in R
> CrossTable(mtcars\$cyl, mtcars\$gear, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)``````

I have added the ‘prop.r’ and the ‘prop.c’ parameters and set them to TRUE here. This is optional but when specified, it gives me the row percentages and the column percentages for each category. Moreover, ‘prop.t’ gives me a table percentage as well. This information is particularly useful when you want to compare one variable with another.

Getting the frequency count is among the very first and most basic steps of a data analysis. Although R gives you many different ways to get the frequency of variables in your data set, I normally find it helpful to get acquainted with a number of methods instead of just one or two. This is because the type of table you need to generate depends largely on what you want to achieve from your data. If you are interested in getting ahead in the field of data science, I encourage you to do your research and read the documentation of R on packages such as ‘gmodels’ and functions such as table().

#### Going Deeper…

Interested in Learning More About Categorical Data Analysis in R? Check Out

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Tutorials

### The Author:

Syed Abdul Hadi is an aspiring undergrad with a keen interest in data analytics using mathematical models and data processing software. His expertise lies in predictive analysis and interactive visualization techniques. Reading, travelling and horse back riding are among his downtime activities. Visit him on LinkedIn for updates on his work.